Update metric_utilities.py

proper subgraph
values->to_numpy

swmmanywhere/metric_utilities.py

@@ -87,12 +87,41 @@ def align_calc_nse(synthetic_results: pd.DataFrame,
                   how='outer').sort_values(by='date')
 
     # Interpolate to time in real data
-    df['value_syn'] = df.set_index('date').value_syn.interpolate().values
+    df['value_syn'] = df.set_index('date').value_syn.interpolate().to_numpy()
     df = df.dropna(subset=['value_real'])
 
     # Calculate NSE
     return nse(df.value_real, df.value_syn)
 
+def create_subgraph(G: nx.Graph,
+                    nodes: list) -> nx.Graph:
+    """Create a subgraph.
+    
+    Create a subgraph of G based on the nodes list. Taken from networkx 
+    documentation: https://networkx.org/documentation/stable/reference/classes/generated/networkx.Graph.subgraph.html
+    
+    Args:
+        G (nx.Graph): The original graph.
+        nodes (list): The list of nodes to include in the subgraph.
+
+    Returns:
+        nx.Graph: The subgraph.
+    """
+    # Create a subgraph SG based on a (possibly multigraph) G
+    SG = G.__class__()
+    SG.add_nodes_from((n, G.nodes[n]) for n in nodes)
+    if SG.is_multigraph():
+        SG.add_edges_from((n, nbr, key, d)
+            for n, nbrs in G.adj.items() if n in nodes
+            for nbr, keydict in nbrs.items() if nbr in nodes
+            for key, d in keydict.items())
+    else:
+        SG.add_edges_from((n, nbr, d)
+            for n, nbrs in G.adj.items() if n in nodes
+            for nbr, d in nbrs.items() if nbr in nodes)
+    SG.graph.update(G.graph)
+    return SG
+
 def nse(y: np.ndarray,
         yhat: np.ndarray) -> float:
     """Calculate Nash-Sutcliffe efficiency (NSE)."""
@@ -133,7 +162,8 @@ def best_outlet_match(synthetic_G: nx.Graph,
     # Subselect the matching graph
     outlet_nodes = [n for n, d in synthetic_G.nodes(data=True) 
                     if d['outlet'] == outlet]
-    return synthetic_G.subgraph(outlet_nodes), outlet
+    sg = create_subgraph(synthetic_G,outlet_nodes)
+    return sg, outlet
 
 def dominant_outlet(G: nx.DiGraph,
                     results: pd.DataFrame) -> tuple[nx.DiGraph,int]:
@@ -165,7 +195,7 @@ def dominant_outlet(G: nx.DiGraph,
                   if d['id'] == max_outlet_arc][0]
 
     # Subselect the matching graph
-    sg = G.subgraph(nx.ancestors(G, max_outlet) | {max_outlet})
+    sg = create_subgraph(G, nx.ancestors(G, max_outlet) | {max_outlet})
     return sg, max_outlet
 
 @metrics.register